# Neuromorphic Spintronics

arXiv: https://arxiv.org/abs/2409.10290 · HTML: https://mlsanity.com/paper/2409.10290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 96 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Memory and information processing in neuromorphic systems](https://mlsanity.com/paper/1506.03264/) (2015)
- [Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware](https://mlsanity.com/paper/1812.01739/) (2018)
- [Memcomputing: Leveraging memory and physics to compute efficiently](https://mlsanity.com/paper/1802.06928/) (2018)
- [Neural-like computing with populations of superparamagnetic basis functions](https://mlsanity.com/paper/1610.09394/) (2016)

## Abstract (arXiv)

Neuromorphic spintronics combines two advanced fields in technology, neuromorphic computing and spintronics, to create brain-inspired, efficient computing systems that leverage the unique properties of the electron's spin. In this book chapter, we first introduce both fields - neuromorphic computing and spintronics and then make a case for neuromorphic spintronics. We discuss concrete examples of neuromorphic spintronics, including computing based on fluctuations, artificial neural networks, and reservoir computing, highlighting their potential to revolutionize computational efficiency and functionality.